Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Gradient Descent: Linear Regression
Graph Chatbot
Related lectures (30)
Deep Learning: Multilayer Perceptron and Training
Log in to Mediaspace to watch this video
Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.
Cross-Validation: Techniques and Applications
Log in to Mediaspace to watch this video
Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.
Linear Regression: Foundations and Applications
Log in to Mediaspace to watch this video
Introduces linear regression, covering its fundamentals, applications, and evaluation metrics in machine learning.
Supervised Learning Fundamentals
Log in to Mediaspace to watch this video
Introduces the fundamentals of supervised learning, including loss functions and probability distributions.
Linear and Logistic Regression
Log in to Mediaspace to watch this video
Introduces linear and logistic regression, covering parametric models, multi-output prediction, non-linearity, gradient descent, and classification applications.
Regression Trees and Ensemble Methods in Machine Learning
Log in to Mediaspace to watch this video
Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.
Linear Models: Continued
Log in to Mediaspace to watch this video
Explores linear models, logistic regression, gradient descent, and multi-class logistic regression with practical applications and examples.
Linear and Weighted Regression: Optimal Parameters and Local Solutions
Log in to Mediaspace to watch this video
Covers linear and weighted regression, optimal parameters, local solutions, SVR application, and regression techniques' sensitivity.
Classification Algorithms: Generative and Discriminative Approaches
Log in to Mediaspace to watch this video
Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.
Linear Models: Part 1
Log in to Mediaspace to watch this video
Covers linear models, including regression, derivatives, gradients, hyperplanes, and classification transition, with a focus on minimizing risk and evaluation metrics.
Previous
Page 2 of 2
Next